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Create HospiNet object from subject database

Source: R/NetworkBuilding.R
hospinet_from_subject_database.Rd

This function creates a HospiNet object from the database containing subjects stays.

Usage

hospinet_from_subject_database(
  base,
  window_threshold = 365,
  count_option = "successive",
  condition = "dates",
  prob_params = c(0.0036, 1/365, 0.128),
  noloops = TRUE,
  nmoves_threshold = NULL,
  flag_vars = NULL,
  flag_values = NULL,
  keep_nodes = FALSE,
  create_MetricsTable = TRUE,
  verbose = FALSE,
  shinySession = NULL,
  ...
)

Arguments

base

(hospinet.base) A database of records of stays of subjects in facilities. This can be obtained using the function checkBase.

window_threshold

(numeric) A threshold for the number of days between discharge and admission to be counted as a transfer. Set to 0 for same day transfer, default is 365 days.

count_option

(character) TODO. Default is "successive".

condition

(character) TODO. Default is "dates".

prob_params

(vector of numeric) Three numerical values to calculate the probability that a movement causes an introduction from hospital A to hospital B. See Donker T, Wallinga J, Grundmann H. (2010) <doi:10.1371/journal.pcbi.1000715> for more details. prob_params[1] is the rate of acquisition in hospital A (related to LOS in hospital A). Default: 0.0036 prob_params[2] is the rate of loss of colonisation (related to time between admissions). Default: 1/365 prob_params[4] is the rate of transmission to other patients in hospital B (related to LOS in hospital B). Default: 0.128

noloops

(boolean). Should transfers within the same nodes (loops) be kept or set to 0. Defaults to TRUE, removing loops (setting matrix diagonal to 0).

nmoves_threshold

(numeric) A threshold for the minimum number of subject transfer between two facilities. Set to NULL to deactivate, default to NULL.

flag_vars

(list) Additional variables that can help flag a transfer, besides the dates of admission and discharge. Must be a named list of two character vectors which are the names of the columns that can flag a transfer: the column that can flag a potential origin, and the column that can flag a potential target. The list must be named with "origin" and "transfer". Eg: list("origin" = "var1", "target" = "var2"). See details.

flag_values

(list) A named list of two character vectors which contain the values of the variables in flag_var that are matched to flag a potential transfer. The list must be named with "origin" and "transfer". The character vectors might be of length greater than one. Eg: list("origin" = c("value1", "value2"), "target" = c("value2", "value2")). The values in 'origin' and 'target' are the values that flag a potential origin of a transfer, or a potential target, respectively. See details.

keep_nodes

(logical) Should nodes with no connections be kept in the edgelist? Defaults to FALSE.

create_MetricsTable

(boolean) Should the metrics table be created along with the network. Setting to FALSE will speed up the results. Default is TRUE.

verbose

TRUE to print computation steps

shinySession

(NULL) internal variable to deal with the progress bar

...

Additional parameters to be sent to checkBase in case the database has not been checked yet.

Value

The function returns a HospiNet object.

Details

This function will build a HospiNet object from a line-listed subject database. The HospiNet object has all of the functions stored as active bindings which can be accessed in the usual way. For more info, see HospiNet. Note that the subject database will need to be run through checkBase before going into this function.

See also

HospiNet

Examples

mydb <- create_fake_subjectDB(n_subjects = 100, n_facilities = 10)
myBase <- checkBase(mydb)
#> Checking for missing values...
#> Checking for duplicated records...
#> Removed 0 duplicates
#> Done.
hospinet_from_subject_database(myBase)
#> 10 facilities and 139 movements.
#> Movement window is 365 days. 
#> Constructing full matrix
#>     f01 f02 f03 f04 f05 f06 f07 f08 f09 f10
#> f01   0   3   2   0   4   1   2   1   0   2
#> f02   2   0   2   2   1   1   1   2   4   0
#> f03   1   1   0   2   1   1   2   1   1   2
#> f04   1   0   3   0   2   2   2   1   2   0
#> f05   4   2   1   1   0   1   0   2   1   3
#> f06   3   1   4   3   1   0   3   3   2   0
#> f07   3   1   0   1   2   2   0   1   1   1
#> f08   1   0   0   4   0   0   1   0   2   2
#> f09   1   1   2   2   2   1   1   0   0   1
#> f10   2   1   1   3   4   1   2   0   2   0